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Available from: Tee Connie, Aug 01, 2015
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    • "System based on palmprint has high user acceptably [18]. Furthermore, palmprint also serves as a reliable biometric features because the print patterns are not same even in mono zygotic twins [5]. Limited work has been reported on palmprint based identification/verification despite of its significant features. "
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    ABSTRACT: This paper proposes a palmprint based verification system which uses low-order Zernike moments of palmprint sub-images. Euclidean distance is used to match the Zernike moments of corresponding sub-images of query and enrolled palmprints. These matching scores of sub-images are fused using a weighted fusion strategy. The proposed system can also classify the sub-image of palmprint into non-occluded or occluded region and verify user with the help of non-occluded regions. So it is robust to occlusion. The palmprint is extracted from the acquired hand image using a low cost flat bed scanner. A palmprint extraction procedure which is robust to hand translation and rotation on the scanner has been proposed. The system is tested on IITK, PolyU and CASIA databases of size 549, 5239 and 7752 hand images respectively. It performs with accuracy of more than 98%, and FAR, FRR less than 2% for all the databases.
    Telecommunication Systems 08/2011; 47(3):275-290. DOI:10.1007/s11235-010-9318-y · 1.16 Impact Factor
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    ABSTRACT: Currently, single sample biometrics recognition (SSBR) has emerged as one of the major research contents. It may lead to bad recognition result. To solve this problem, we present a novel approach by fusing two kinds of hand-based biometrics, i.e., palmprint and middle finger. We obtain their discriminant features by combining statistical information and structural information of each modal which are extracted using locality preserving projection (LPP) based on wavelet transform (WT). In order to reduce the influence of affine transform, we utilize mean filtering to enhance the robustness of structural information to improve the discriminant ability of palmprint high-frequency sub-bands. The two types of features are then fused at score level for the final hand-based SSBR. The experiments on the hand image database that contains 1,000 samples from 100 individuals show that the proposed feature extraction and fusion methods lead to promising performance. KeywordsSingle sample biometrics recognition–Palmprint and middle finger biometrics–Wavelet transform–Structural feature enhancement–Feature fusion
    Neural Computing and Applications 11/2011; DOI:10.1007/s00521-011-0521-x · 1.76 Impact Factor
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    ABSTRACT: In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.
    Neural Computing and Applications 05/2012; 22(1). DOI:10.1007/s00521-011-0800-6 · 1.76 Impact Factor
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